ai-app-performance-optimization

To build successful AI applications, move beyond the "ideal crisis" of chasing every new research paper or model update. Real performance gains come from traditional product engineering applied to AI: data quality, workflow optimization, and user-centric evaluation.

The Optimization Pivot Differentiate between activities that offer marginal gains (The Hype) and those that drive exponential product quality (The Reality).

Optimize Data for Retrieval (RAG) If using Retrieval-Augmented Generation (RAG), the biggest lever for quality is not the retrieval algorithm, but how the data is prepared.

AI의 과장된 광고(최신 모델, 벡터 DB, 에이전트 프레임워크)에서 실제로 제품 품질을 향상시키는 활용도가 높은 활동으로 초점을 전환하세요. AI 기능의 성능이 저하되거나, 팀이 "연구 루프"에 갇히거나, 새로운 AI 애플리케이션에 대한 로드맵을 계획할 때 이 기능을 사용하세요. 출처: samarv/shanon.